Applying functions to dataframes¶

By now, you know that you need to load the standard libraries for plotting. I am going to start hiding this code for now on.

import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
sns.set(rc={"figure.dpi":100, 'savefig.dpi':300})
sns.set_context('notebook')
sns.set_style("ticks")
from IPython.display import set_matplotlib_formats
set_matplotlib_formats('retina', 'svg')

Let’s download the temp_price.csv dataset we introdcued in The Python data analysis library.

import requests
import os
def download(url, local_filename=None):
    """
    Downloads the file in the ``url`` and saves it in the current working directory.
    """
    data = requests.get(url)
    if local_filename is None:
        local_filename = os.path.basename(url)
    with open(local_filename, 'wb') as fd:
        fd.write(data.content)
   
# The url of the file we want to download
url = 'https://raw.githubusercontent.com/PurdueMechanicalEngineering/me-297-intro-to-data-science/master/data/temp_price.csv'
download(url)

Let’s load it and clean it as we did before:

import pandas as pd
temp_price = pd.read_csv('temp_price.csv')
temp_no_null_rows = temp_price.dropna(axis=0)
clean_data = temp_no_null_rows.rename(columns={'Price per week': 'week_price',
                                               'Price per day': 'daily_price'})
clean_data.head()

	household	date	score	t_out	t_unit	hvac	price	week_price	daily_price
0	a1	2019-01-06	85	38.599231	71.580704	35.113758	0.17303	6.075734	0.867962
1	a10	2019-01-06	70	38.599231	73.286260	63.949057	0.17303	11.065105	1.580729
2	a11	2019-01-06	61	38.599231	74.252046	147.612108	0.17303	25.541323	3.648760
3	a12	2019-01-06	65	38.599231	73.708482	74.394518	0.17303	12.872483	1.838926
4	a13	2019-01-06	66	38.599231	73.549554	173.095836	0.17303	29.950772	4.278682

We are going to change units of temperature from degrees F to degrees C. We will use the function pandas.DataFrame.apply() to achieve this. First, let us make a copy of the data frame:

new_price_data = clean_data.copy()

Now we need to define the function that changes from degrees F to degrees C:

def change_degrees_F_to_C(deg_F):
    """
    Changes the temperature from degrees F to degrees C.
    """
    return (deg_F - 32) * 5 / 9

Now we need to update the two columns week_price and daily_price. This is a very elegant way to do it.

clean_data[['t_out', 't_unit']].apply(change_degrees_F_to_C)

	t_out	t_unit
0	3.66624	21.989280
1	3.66624	22.936811
2	3.66624	23.473359
3	3.66624	23.171379
4	3.66624	23.083085
5	3.66624	22.414698
6	3.66624	27.488040
7	3.66624	24.880280
8	3.66624	22.866871
9	3.66624	23.347305
10	3.66624	21.884039
11	3.66624	24.847084
12	3.66624	23.770820
13	3.66624	24.484086
14	3.66624	23.297591
15	3.66624	24.337357
16	3.66624	22.750551
17	3.66624	21.637511
18	3.66624	23.686701
19	3.66624	24.048280
20	3.66624	24.305156
21	3.66624	25.893959
22	3.66624	25.940972
23	3.66624	23.154225
24	3.66624	25.505263
25	3.66624	24.003582
27	3.66624	21.964906
28	3.66624	22.496552
29	3.66624	25.355489
30	3.66624	21.614005
32	3.66624	22.715719
34	3.66624	21.346781
35	3.66624	24.490217
36	3.66624	23.475860
37	3.66624	23.798280
38	3.66624	23.262373
39	3.66624	24.211034
40	3.66624	20.633749
42	3.66624	25.585772
43	3.66624	25.672784
44	3.66624	23.767251
45	3.66624	22.729856
46	3.66624	22.692419
47	3.66624	23.679756
48	3.66624	22.648424
49	3.66624	24.263090

In this way, we applied the function to each entry of the the clean_data[['t_out', 't_unit']]. However, this change was not made to the clean_data dataframe. To make the change stick to the old data frame you need to do it like this:

clean_data_in_deg_C = clean_data.copy()
clean_data_in_deg_C[['t_out', 't_unit']] = clean_data[['t_out', 't_unit']].apply(change_degrees_F_to_C)
clean_data_in_deg_C.head()

	household	date	score	t_out	t_unit	hvac	price	week_price	daily_price
0	a1	2019-01-06	85	3.66624	21.989280	35.113758	0.17303	6.075734	0.867962
1	a10	2019-01-06	70	3.66624	22.936811	63.949057	0.17303	11.065105	1.580729
2	a11	2019-01-06	61	3.66624	23.473359	147.612108	0.17303	25.541323	3.648760
3	a12	2019-01-06	65	3.66624	23.171379	74.394518	0.17303	12.872483	1.838926
4	a13	2019-01-06	66	3.66624	23.083085	173.095836	0.17303	29.950772	4.278682

Question¶

Write code that changes the hvac column from kWh’s to MJ (megaJoule).

# your code here

Introduction to Data Science for Mechanical Engineers (Lecture Book)

Applying functions to dataframes

Contents

Applying functions to dataframes¶

Question¶